Thomas, EM;Greene, BR;Lightbody, G;Marnane, WP;Boylan, GB

2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8

Seizure Detection in Neonates: Improved Classification through Supervised Adaptation

2008

January

Validated

1

WOS: 3 ()

EEG ECG

903

The goal of neonatal seizure detection Is the development of a patient independent system to alert staff in the neonatal Intensive care unit of ongoing seizures. This study demonstrates the potential in adapting a patient independent classifier using patient specific data. Supervised adaptation is Investigated using the basic gradient descent algorithm and least mean squares procedures. An increase in mean ROC area of 3% is obtained for the best performing learning algorithm, yielding an increase in mean accuracy of 7.7% compared to the patient independent algorithm.